Applied Data Scientist

Description

Personetics is the global leader in financial data-driven personalization, helping financial institutions deepen client relationships through enhanced financial wellness. We serve 130+ financial institutions and reach 150 million customers across 35 global markets, with offices in New York, London, Singapore, São Paulo, and Tel Aviv.

About The Position

We are looking for a skilled Applied Data Scientist to drive impactful ML/DL research, model development, and optimization. This role involves designing, building, and deploying innovative machine learning models while ensuring seamless production integration and ongoing model performance monitoring.

If you enjoy solving real-world challenges through cutting-edge data science and want to work on impactful projects, we’d love to hear from you!

Responsibilities

Research & Model Development:

• Design and develop innovative ML models aligned with the company’s strategic objectives.
• Conduct exploratory data analysis, feature engineering, and model specification documentation.
• Implement model optimization techniques to enhance performance and minimize drift.

Model Deployment & Monitoring:

• Ensure seamless MVP finalization and transition of models into production.
• Build robust monitoring pipelines for ongoing model evaluation and reliability.
• Troubleshoot and enhance existing models by integrating new features and improving efficiency.

Cross-Functional Collaboration:

• Work closely with Product Managers, Engineers, and stakeholders to align AI initiatives with business needs.
• Effectively communicate complex data science concepts to non-technical audiences.

Requirements

• 2+ years in a Data Scientist or related role.
• Strong proficiency in Python (Pandas, NumPy, PyTorch).
• Experience with tabular data (preprocessing, cleaning, and feature engineering).
• Strong analytical and problem-solving skills.
• Ability to work in fast-paced, product-driven environments.

Nice to have

• Experience in FinTech or banking (preferred but not required).
• Familiarity with Large Language Models (LLMs) and their applications.
• Experience with cloud platforms (AWS, Azure, GCP).